CN111368261A - Quantitative and qualitative description method for impervious surface index based on atmospheric correction - Google Patents
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Abstract
The invention discloses a quantitative and qualitative description method for impervious surface indexes based on atmospheric correction, which comprises the following steps: the method comprises the steps of carrying out data preprocessing on Landsat8 remote sensing images of a research area by the existing method, researching remote sensing data based on different radiation correction levels (radiation calibration and FLASSH atmospheric correction), taking the main city of Chongqing as the research area, extracting different types of building indexes by utilizing the gray value (Digital Number, DN), the apparent (TOA) reflectivity and the Surface (Surface) reflectivity of Landsat-8 images, carrying out detailed contrast analysis on NDBI, IBI, UI and BUAI impervious Surface index change characteristics, distribution range, correlation coefficient and extraction precision of three different processing stages of gray value, radiation calibration and atmospheric correction, and evaluating the influence of the atmospheric correction on the inverted building index performance difference. The method can provide certain research significance and application value for extracting the information of the impervious surface influenced by remote sensing.
Description
Technical Field
The invention belongs to the field of urban impervious surface information. Specifically, 4 impervious surface indexes of three different processing stages of gray value, radiometric calibration and atmospheric correction are inverted, and the influence of atmospheric correction on the performance difference of the impervious surface indexes is further quantitatively analyzed.
Background
The impervious surface index is a remote sensing technology for extracting urban impervious surface distribution and reflecting dynamic expansion of cities in the field of remote sensing, and scholars at home and abroad carry out a great deal of research. However, the remote sensing image is usually interfered by the sensor itself and multiple factors such as atmosphere and aerosol, so it is necessary to extract the urban impervious surface index through atmospheric correction.
The remote sensing data inversion impervious surface index is mostly expressed by a DN value, the DN value is a pixel brightness value of a remote sensing image, and a gray value of a ground object is recorded without a unit. The TOA reflectivity is the ratio of the amount of reflected radiation to the amount of incident radiation on the ground, and the Surface reflectivity is the reflectivity of the top of the atmospheric layer, referred to as the apparent reflectivity. The reflectivity participating in the calculation of the urban impervious Surface index can be a DN value and a TOA reflectivity, and can also be a Surface reflectivity after atmospheric correction. A large number of researches show that the vegetation index can be calculated based on the gray value, the apparent reflectivity or the earth surface reflectivity of the remote sensing image, but the vegetation index calculated according to the earth surface radiance value or the reflectivity after radiometric calibration and atmospheric correction is most accurate. At present, scholars perform comparative analysis on the vegetation index characteristics under different radiation positive levels, perform detailed comparative analysis on NDVI change characteristics extracted under different radiation levels in space and time by adopting quantitative remote sensing and regression analysis methods, and quantitatively evaluate the influence of atmosphere on the extracted vegetation index. The learners also study whether atmospheric correction is carried out or not and the influence of NDVI threshold selection on the remote sensing extraction precision of the green tide area.
At present, most scholars extract the impervious surface index of the city, and conveniently and directly use DN value inversion of an original image without considering whether atmospheric correction is helpful to improve the impervious surface index performance. The comprehensive analysis and comparison research on the performance of the waterproofing Surface index inverted under the DN value, the TOA reflectivity and the Surface reflectivity is less, and meanwhile, the influence of atmospheric correction on the extraction of the urban waterproofing Surface index is lack of attention, so that the further comparison research on the results obtained by different processing methods is necessary. The distribution range, the change characteristics, the correlation coefficient and the extraction precision of a plurality of representative impervious Surface indexes inverted on a gray value (DN), an apparent (TOA) reflectivity and a Surface (Surface) reflectivity are contrastively analyzed by utilizing Landsat-8 remote sensing data, the influence of atmospheric correction on the performance difference of the building indexes is discussed, and reference are provided for the research of extracting the building indexes from the Landsat-8 data in the future.
Disclosure of Invention
The present invention is directed to solving the above problems of the prior art. A quantitative and qualitative description method for impervious surface indexes based on atmospheric correction is provided. The technical scheme of the invention is as follows:
a quantitative and qualitative description method for impervious surface index based on atmospheric correction comprises the following steps:
1) acquiring a Landsat8 remote sensing image of a research area;
2) preprocessing the original remote sensing image data, including atmospheric correction, radiometric calibration and geometric correction;
3) obtaining remote sensing data of radiometric calibration and FLASSH atmospheric correction, and obtaining a gray value DN, an apparent TOA reflectivity and a Surface reflectivity of the Landsat-8 image after radiometric calibration and atmospheric correction;
4) inverting 4 impervious surface indexes of three different processing stages of gray value, radiometric calibration and atmospheric correction;
5) obtaining 4 impervious surface index change characteristics and distribution ranges of three different treatment stages by adopting a mathematical statistical method;
6) and 4 impervious surface index correlation coefficients and extraction precision of three different processing stages are obtained by adopting a mathematical statistical method, a fitting method, a double-window variable-step searching method and a threshold value selecting method.
Further, the Landsat-8 remote sensing image of the research area in the step 1) is purchased or downloaded from a professional website.
Further, the step 2) of preprocessing the original remote sensing image data includes atmospheric correction, radiometric calibration and geometric correction, and specifically includes:
adopting the existing software including ENVI5.3, carrying out geometric fine correction processing on all wave bands including thermal infrared wave bands in the original image under the assistance of Landsat-8 satellite images, then carrying out radiometric calibration, and then carrying out atmospheric correction on the surface reflectivity of each wave band by utilizing an ENVI-FLASSH tool to obtain surface reflectivity data after atmospheric correction.
Further, the step 3) of obtaining the remote sensing data of radiometric calibration and FLASSH atmospheric correction specifically includes: using the formula LλRadiometric calibration with gain DN + offset, LλRepresenting the scaled apparent reflectivity, gain being the gain value; the offset is an offset value, then FLASSH atmospheric correction is carried out, the basis of the FLASSH atmospheric correction is a Modtran model, the Modtran model is derived from an atmospheric radiation transmission equation, and the calculation formula is as follows:
la is the atmospheric path radiation component of the radiation brightness and is the result of the action of atmospheric molecules and aerosol, rho is the reflectivity of the surface of the pixel, rhoeThe values of the parameters A, B, S and La are calculated and obtained through a radiation transmission model MODTRAN, and finally the gray value, the apparent reflectivity and the surface reflectivity of the Landsat-8 image are obtained.
Further, the step 4) of inverting normalized building indexes NDBI, novel building land index IBI, city index UI and city built-up area index BUAI of three different processing stages of gray value inversion, radiometric calibration and atmospheric correction specifically includes 4 impervious surface indexes:
BUAI＝NDBI-NDVI
green is a Green light wave band, Red is a Red light wave band, NIR is a near infrared wave band, SWIR1 is a short wave infrared wave band 1, SWIR2 is a short wave infrared wave band 2, NDVI is a normalized vegetation index, and for Landsat-8 images, Blue, Green, Red, NIR, SWIR1 and SWIR2 respectively correspond to wave bands TM2, TM 3, TM 4, TM 5, TM 6 and TM 7.
Further, the step 5) obtains 4 impervious surface index change characteristics and distribution ranges of three different treatment stages by using a mathematical statistical method, and specifically includes:
(1) selecting a large number of NDBI, IBI, UI and BUAI impervious surface index sample points in three different processing stages of gray value, radiometric calibration and atmospheric correction;
(2) importing the sample points into Excel statistical variation characteristics;
(3) and leading the sample points into a Matlab statistical distribution range.
Further, the step 6) obtains 4 kinds of impervious surface exponential correlation coefficients and extraction precision at three different processing stages by adopting a mathematical statistic method, a fitting method, a double-window variable-step search method and a threshold selection method, and specifically comprises the following steps:
(1) selecting a large number of NDBI, IBI, UI and BUAI impervious surface index sample points in three different processing stages of gray value, radiometric calibration and atmospheric correction;
(2) introducing the sample points into a Matlab fitting correlation coefficient and a linear equation;
(3) and (3) obtaining the optimal threshold value of each index in three different processing stages by utilizing a double-window variable-step-size searching method to select a threshold value method, and finally carrying out precision evaluation on each index.
The invention has the following advantages and beneficial effects:
the invention extracts NDBI, IBI, UI and BUAI impervious Surface index precisions under DN value, TOA reflectivity and Surface reflectivity respectively by Landsat-8 remote sensing, and comprehensively evaluates the impervious Surface index performance difference under three conditions.
The histograms of NDBI, IBI, UI and BUAI are changed under 3 different radiation levels, the range of the histogram interval under the Surface reflectivity is enlarged, the peak value and the trough value are more obvious than those before atmospheric correction, and the histogram curve is smoother, which shows that the atmospheric correction eliminates the influence of atmospheric, illumination and other factors on the Surface feature reflectivity. The water impermeability index distribution ranges of NDBI, IBI, UI and BUAI show that the variation trends of the minimum value, the maximum value, the mean value and the standard deviation under three different radiation levels are basically the same. The minimum value of the four impervious surface indexes is reduced, the maximum value is increased, the value range is gradually increased, the impervious surface information is enhanced, and the impervious surface index inverted after atmospheric correction is improved. The standard deviation of the four impervious Surface indexes is the largest under the Surface reflectivity, the larger the standard deviation is, the more dispersed the gray level distribution is, the better the visual effect of the image is, and the richer the information content of the image is, so the atmospheric correction is more favorable for the impervious Surface information extraction. The impervious Surface indexes under TOA reflectivity and Surface reflectivity and DN value have obvious linear correlation and obvious difference. The Accuracy of extracting the impervious Surface objects under the DN value, the TOA reflectivity and the Surface reflectivity is different by utilizing NDBI, IBI, UI and BUAI, the Accuracy of extracting the impervious Surface objects under the Surface reflectivity is higher, and the Accuracy of the NDBI, IBI, UI and BUAI under the Surface reflectivity is improved compared with the Accuracy of the DN value by utilizing the over access and Kappa coefficients, so that the atmospheric correction is beneficial to improving the Accuracy of extracting the impervious Surface information. .
The innovation point of the invention is mainly the steps (5) and (6). At present, most scholars extract urban impervious surface information, and conveniently and directly use DN values of original images for inversion, and whether atmospheric correction is helpful for improving impervious surface index performance is not considered.
(1) The atmospheric correction eliminates the influence of factors such as atmosphere and illumination on the reflectivity of the ground object.
(2) The standard deviation is the largest after atmospheric correction, the larger the standard deviation is, the more dispersed the gray level distribution is, the better the visual effect of the image is, and the richer the information content of the image is, so that the atmospheric correction is more favorable for extracting the building information.
(3) There is a clear linear correlation, and sometimes a interconversion, between the building indices at TOA and Surface reflectivities and DN values.
(4) The NDBI index, the IBI index, the UI index and the BUAI index have improved accuracy under the Surface reflectivity compared with the OverallAccuracy and Kappa coefficients under the DN value, and the atmospheric correction is helpful for improving the accuracy of building extraction.
Drawings
FIG. 1 is a flow chart of a method for providing a quantitative and qualitative description of the effect of atmospheric corrections on the variation in the waterproofing surface index performance according to a preferred embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be described in detail and clearly with reference to the accompanying drawings. The described embodiments are only some of the embodiments of the present invention.
The technical scheme for solving the technical problems is as follows:
the Landsat8 remote sensing images of the study area obtained in step (1) above were purchased or downloaded from some professional websites.
Further, in the step (2), by means of existing software including ENVI5.3, with the assistance of the Landsat-8 satellite image, geometric fine correction processing is performed on all bands including a thermal infrared band in the original image, and then atmospheric correction is performed on the surface reflectivity of each band by using an ENVI-flatsh tool, so as to obtain surface reflectivity data after atmospheric correction.
Further, the above step (3) utilizes radiometric calibration and atmospheric correction methods, and the steps include: the radiation calibration is carried out by using the formula L lambda ═ gain DN + offset, and when FLASSH atmosphere correction is carried out, the basis of the FLASSH atmosphere correction is a Modtran model, and the Modtran model is derived from an atmosphere radiation transmission equation, so that the atmosphere correction effect is better. The calculation formula is as follows:
finally, the gray value (DN), the apparent (TOA) reflectivity and the Surface (Surface) reflectivity of the Landsat-8 image are obtained according to the formula.
Further, the step (4) inverts 4 impervious surface indexes of NDBI, IBI, UI and BUAI at three different processing stages of gray value, radiometric calibration and atmospheric correction.
Further, the inversion of 4 water planes in the step (5) mentioned above seems to specifically include: the two peak values and the trough values of the histogram of the image after atmospheric correction are more obvious than those before atmospheric correction, the histogram curve is smoother, the threshold value judgment is facilitated, and the value ranges of the four building indexes under the DN value, the TOA reflectivity and the Surface reflectivity are gradually enlarged according to the statistical characteristics. And the inversion impervious Surface index mean value is minimum under the TOA reflectivity, the inversion impervious Surface index standard deviation is maximum under the Surface reflectivity, and finally the influence of atmospheric correction on the impervious Surface index performance difference is obtained.
Further, in the step (6), by analyzing the influence of the atmospheric correction established in the step (5) on the performance difference of the impervious Surface index, the building index data under the TOA reflectivity and the Surface reflectivity and the building index of the original image have strong positive correlation coefficients, and are obviously different from each other, the overall accuracy of the building index under the DN value, the TOA reflectivity and the Surface reflectivity is different from the Kappa value, and the overall accuracy of the building index after the atmospheric correction is correspondingly improved from the Kappa value.
The above examples are to be construed as merely illustrative and not limitative of the remainder of the disclosure. After reading the description of the invention, the skilled person can make various changes or modifications to the invention, and these equivalent changes and modifications also fall into the scope of the invention defined by the claims.
Claims (7)
1. A quantitative and qualitative description method for impervious surface index based on atmospheric correction is characterized by comprising the following steps:
1) acquiring a Landsat8 remote sensing image of a research area;
2) preprocessing the original remote sensing image data, including atmospheric correction, radiometric calibration and geometric correction;
3) obtaining remote sensing data of radiometric calibration and FLASSH atmospheric correction, and obtaining a gray value DN, an apparent TOA reflectivity and a Surface reflectivity of the Landsat-8 image after radiometric calibration and atmospheric correction;
4) inverting 4 impervious surface indexes of three different processing stages of gray value, radiometric calibration and atmospheric correction;
5) obtaining 4 impervious surface index change characteristics and distribution ranges of three different treatment stages by adopting a mathematical statistical method;
6) and 4 impervious surface index correlation coefficients and extraction precision of three different processing stages are obtained by adopting a mathematical statistical method, a fitting method, a double-window variable-step searching method and a threshold value selecting method.
2. The method for quantitatively and qualitatively describing the impervious surface index based on the atmospheric correction as claimed in claim 1, wherein the Landsat-8 remote sensing image of the research area in the step 1) is purchased or downloaded from a professional website.
3. The method for quantitatively and qualitatively describing the impervious surface index based on the atmospheric correction as claimed in claim 1 or 2, wherein the step 2) is used for preprocessing the original remote sensing image data, including atmospheric correction, radiometric calibration and geometric correction, and specifically comprises the following steps:
adopting the existing software including ENVI5.3, carrying out geometric fine correction processing on all wave bands including thermal infrared wave bands in the original image under the assistance of Landsat-8 satellite images, then carrying out radiometric calibration, and then carrying out atmospheric correction on the surface reflectivity of each wave band by utilizing an ENVI-FLASSH tool to obtain surface reflectivity data after atmospheric correction.
4. The method for quantitatively and qualitatively describing the impervious surface index based on the atmospheric correction as claimed in claim 3, wherein the step 3) of obtaining the remote sensing data of radiometric calibration and FLASSH atmospheric correction specifically comprises the following steps: using the formula LλRadiometric calibration with gain DN + offset, LλRepresenting the scaled apparent reflectivity, gain being the gain value; offset is an offset value and is then enteredThe FLASSH atmospheric correction is based on a Modtran model, and the Modtran model is derived from an atmospheric radiation transmission equation, and the calculation formula is as follows:
la is the atmospheric path radiation component of the radiation brightness and is the result of the action of atmospheric molecules and aerosol, rho is the reflectivity of the surface of the pixel, rhoeThe values of the parameters A, B, S and La are calculated and obtained through a radiation transmission model MODTRAN, and finally the gray value, the apparent reflectivity and the surface reflectivity of the Landsat-8 image are obtained.
5. The method for quantitatively and qualitatively describing the impervious surface index based on the atmospheric correction as claimed in claim 4, wherein the step 4) inverts the normalized building index NDBI, the novel building land index IBI, the city index UI and the city built-up area index BUAI4 impervious surface indexes of three different processing stages of gray value, radiometric calibration and atmospheric correction, and specifically comprises:
BUAI＝NDBI-NDVI
green is a Green light wave band, Red is a Red light wave band, NIR is a near infrared wave band, SWIR1 is a short wave infrared wave band 1, SWIR2 is a short wave infrared wave band 2, NDVI is a normalized vegetation index, and for Landsat-8 images, Blue, Green, Red, NIR, SWIR1 and SWIR2 respectively correspond to wave bands TM2, TM 3, TM 4, TM 5, TM 6 and TM 7.
6. The method for quantitatively and qualitatively describing the impervious surface index based on the atmospheric correction as claimed in claim 5, wherein the step 5) adopts a mathematical statistics method to obtain the change characteristics and distribution range of the impervious surface index of 4 different treatment stages, and specifically comprises the following steps:
(1) selecting a large number of NDBI, IBI, UI and BUAI impervious surface index sample points in three different processing stages of gray value, radiometric calibration and atmospheric correction;
(2) importing the sample points into Excel statistical variation characteristics;
(3) and leading the sample points into a Matlab statistical distribution range.
7. The method for quantitatively and qualitatively describing the impervious surface index based on the atmospheric correction as claimed in claim 6, wherein the step 6) adopts a mathematical statistic method, a fitting method, a double-window variable step size searching method and a threshold selection method to obtain the relevant coefficients and the extraction precision of the impervious surface index of 4 different processing stages, and specifically comprises the following steps:
(1) selecting a large number of NDBI, IBI, UI and BUAI impervious surface index sample points in three different processing stages of gray value, radiometric calibration and atmospheric correction;
(2) introducing the sample points into a Matlab fitting correlation coefficient and a linear equation;
(3) and (3) obtaining the optimal threshold value of each index in three different processing stages by utilizing a double-window variable-step-size searching method to select a threshold value method, and finally carrying out precision evaluation on each index.
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